Entity resolution with evolving rules

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Entity resolution (ER) identifies database records that refer to the same real world entity. In practice, ER is not a one-time process, but is constantly improved as the data, schema and application are better understood. We address the problem of keeping the ER result up-to-date when the ER logic "evolves" frequently. A naïve approach that re-runs ER from scratch may not be tolerable for resolving large datasets. This paper investigates when and how we can instead exploit previous "materialized" ER results to save redundant work with evolved logic. We introduce algorithm properties that facilitate evolution, and we propose efficient rule evolution techniques for two clustering ER models: match-based clustering and distance-based clustering. Using real data sets, we illustrate the cost of materializations and the potential gains over the naïve approach. © 2010 VLDB Endowment.
Publisher
The VLDB Endowment
Issue Date
2010-09
Language
English
Citation

36th International Conference on Very Large Data Bases, VLDB 2010, pp.1326 - 1337

ISSN
2150-8097
DOI
10.14778/1920841.1921004
URI
http://hdl.handle.net/10203/260222
Appears in Collection
EE-Conference Papers(학술회의논문)
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